Clinical Utility of Screening for Anxiety and Depression in Children with Tourette Syndrome.
Bibliographic record
Abstract
BACKGROUND: Tourette syndrome (TS) is often co-morbid with attention deficit hyperactivity disorder (ADHD) and obsessive compulsive disorder (OCD). Studies of TS, anxiety and depression have found variable results depending on study methodology and sample characteristics. Our aim was to examine the clinical utility of routine screening for anxiety and depression in children with TS. METHODS: Using a clinic-based sample, we evaluated the proportion of children with TS meeting diagnostic criteria for ADHD, OCD, generalized anxiety disorder (GAD), separation anxiety disorder (SAD), and major depressive disorder (MDD); the frequency of above average anxiety and depressive symptoms using the Multidimensional Anxiety Scale for Children (MASC) and the Children's Depression Inventory (CDI); and the association between diagnoses and symptom severity. RESULTS: One hundred twenty six children were included (mean age 10.7 years). The most common comorbid disorder was ADHD (37%), followed by GAD (21%), OCD (10%), MDD (2%) and SAD (2%). On the MASC, the separation anxiety/panic subscale score was higher than all other subscale scores (p<0.0001). Clinically significant anxiety symptoms were present in 20% of the sample based on the MASC Anxiety Disorders Index, while 6% were identified as potentially clinically depressed based on the CDI Total Score. Yale Global Tic Severity Scale scores were positively correlated with total scores on the MASC (r=0.22, p=0.03) and CDI (r=0.37, p=0.0002). CONCLUSIONS: Routine screening children with TS for anxiety is warranted given the rate of comorbidity. Screening for depression in TS will have a higher yield in adolescents, adults, and children with more severe tics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".